# import the library that allow us manipulate with arrays
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
# accuracy score check how our model is performing
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
import matplotlib.pyplot as plt
from sklearn.datasets import make_blobs
import hvplot.pandas
import seaborn as sns
from scipy import stats

pip install channels

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pip install xgboost

Collecting xgboost
Note: you may need to restart the kernel to use updated packages.  Downloading xgboost-1.5.1-py3-none-win_amd64.whl (106.6 MB)

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Installing collected packages: xgboost
Successfully installed xgboost-1.5.1

!pip install -q hvplot

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#loading the heart_patient_data we have
heart_data=pd.read_csv('heart.csv')

# showing the first rows of the data to make sure it is saved
heart_data.head()

	age	sex	cp	trestbps	chol	fbs	restecg	thalach	exang	oldpeak	slope	ca	thal	target
0	63	1	3	145	233	1	0	150	0	2.3	0	0	1	1
1	37	1	2	130	250	0	1	187	0	3.5	0	0	2	1
2	41	0	1	130	204	0	0	172	0	1.4	2	0	2	1
3	56	1	1	120	236	0	1	178	0	0.8	2	0	2	1
4	57	0	0	120	354	0	1	163	1	0.6	2	0	2	1

# showing the last five rows in our data and now we know the number of the rows the dataset is having which is 302 rows
heart_data.tail()

	age	sex	cp	trestbps	chol	fbs	restecg	thalach	exang	oldpeak	slope	ca	thal	target
298	57	0	0	140	241	0	1	123	1	0.2	1	0	3	0
299	45	1	3	110	264	0	1	132	0	1.2	1	0	3	0
300	68	1	0	144	193	1	1	141	0	3.4	1	2	3	0
301	57	1	0	130	131	0	1	115	1	1.2	1	1	3	0
302	57	0	1	130	236	0	0	174	0	0.0	1	1	2	0

# trying to figure out the shape of the datasets

heart_data.shape

(303, 14)

#getting the full information we want to know about the datasets including datatypes and every detail
heart_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 303 entries, 0 to 302
Data columns (total 14 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   age       303 non-null    int64  
 1   sex       303 non-null    int64  
 2   cp        303 non-null    int64  
 3   trestbps  303 non-null    int64  
 4   chol      303 non-null    int64  
 5   fbs       303 non-null    int64  
 6   restecg   303 non-null    int64  
 7   thalach   303 non-null    int64  
 8   exang     303 non-null    int64  
 9   oldpeak   303 non-null    float64
 10  slope     303 non-null    int64  
 11  ca        303 non-null    int64  
 12  thal      303 non-null    int64  
 13  target    303 non-null    int64  
dtypes: float64(1), int64(13)
memory usage: 33.3 KB

#checking whether we are having missing values in the datasets in order to check the manipulation
heart_data.isnull().sum()

age         0
sex         0
cp          0
trestbps    0
chol        0
fbs         0
restecg     0
thalach     0
exang       0
oldpeak     0
slope       0
ca          0
thal        0
target      0
dtype: int64

# showing the stastical measure about the data we are having like the standard deviations, the mean, median and all the stastical 
# measures we are having
# for example here we are having the mean value of all the columns with the minimum and maximum values of each row
# other point we now know that 25 % of the persons are under 47.5 age
heart_data.describe()

	age	sex	cp	trestbps	chol	fbs	restecg	thalach	exang	oldpeak	slope	ca	thal	target
count	303.000000	303.000000	303.000000	303.000000	303.000000	303.000000	303.000000	303.000000	303.000000	303.000000	303.000000	303.000000	303.000000	303.000000
mean	54.366337	0.683168	0.966997	131.623762	246.264026	0.148515	0.528053	149.646865	0.326733	1.039604	1.399340	0.729373	2.313531	0.544554
std	9.082101	0.466011	1.032052	17.538143	51.830751	0.356198	0.525860	22.905161	0.469794	1.161075	0.616226	1.022606	0.612277	0.498835
min	29.000000	0.000000	0.000000	94.000000	126.000000	0.000000	0.000000	71.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	47.500000	0.000000	0.000000	120.000000	211.000000	0.000000	0.000000	133.500000	0.000000	0.000000	1.000000	0.000000	2.000000	0.000000
50%	55.000000	1.000000	1.000000	130.000000	240.000000	0.000000	1.000000	153.000000	0.000000	0.800000	1.000000	0.000000	2.000000	1.000000
75%	61.000000	1.000000	2.000000	140.000000	274.500000	0.000000	1.000000	166.000000	1.000000	1.600000	2.000000	1.000000	3.000000	1.000000
max	77.000000	1.000000	3.000000	200.000000	564.000000	1.000000	2.000000	202.000000	1.000000	6.200000	2.000000	4.000000	3.000000	1.000000

# now seeing the distribution of the target column in the datasets
heart_data['target'].value_counts()

1    165
0    138
Name: target, dtype: int64

# we see, now we are having that 165 that are having a disease and 138 are healthy, the distribution is not very high from each other
# as the distribution in one class is not very large more than the other class, so we don't need preprocessing data

heart_data.target.value_counts().hvplot.bar(
    title="Heart Disease Patients", xlabel='Heart Disease', ylabel='The numbers of the patients found', 
    width=500, height=350
)

# now getting the graph of the patient distribution on the graph

# trying to graph the healthy and pateint againtst the sex classification

Non_healthy_patient=heart_data.loc[heart_data['target']==1,'sex'].value_counts().hvplot.bar(alpha=0.4)

healthy_patient=heart_data.loc[heart_data['target']==0,'sex'].value_counts().hvplot.bar(alpha=0.4)

(Non_healthy_patient*healthy_patient).opts(
title='relation between heart disease and sex',xlabel='sex',ylabel='numbers',width=450,height=450,legend_cols=2)

# trying to graph the healthy and pateint againtst the chest pain classification

Non_healthy_patient_chest_pain=heart_data.loc[heart_data['target']==1,'cp'].value_counts().hvplot.bar(alpha=0.4)

healthy_patient_chest_pain=heart_data.loc[heart_data['target']==0,'cp'].value_counts().hvplot.bar(alpha=0.4)

(Non_healthy_patient_chest_pain*healthy_patient_chest_pain).opts(
title='relation between heart disease and chest pain',xlabel='chest pain',ylabel='numbers',width=450,height=450,legend_cols=2)

# trying to graph the healthy and pateint againtst the blood sugar classification

Non_healthy_patient_fasting_blood_sugar=heart_data.loc[heart_data['target']==1,'fbs'].value_counts().hvplot.bar(alpha=0.4)

healthy_patient_fasting_blood_suga=heart_data.loc[heart_data['target']==0,'fbs'].value_counts().hvplot.bar(alpha=0.4)

(Non_healthy_patient_fasting_blood_sugar*healthy_patient_fasting_blood_suga).opts(
title='relation between heart disease and fasting blood sugar',xlabel='fbs',ylabel='numbers',width=450,height=450,legend_cols=2)

# trying to graph the healthy and pateint againtst the resting electrocardiograhic classification

Non_healthy_patient_resting_electrocardiograhic=heart_data.loc[heart_data['target']==1,'restecg'].value_counts().hvplot.bar(alpha=0.4)

healthy_patientt_resting_electrocardiograhic=heart_data.loc[heart_data['target']==0,'restecg'].value_counts().hvplot.bar(alpha=0.4)

(Non_healthy_patient_resting_electrocardiograhic*healthy_patientt_resting_electrocardiograhic).opts(
title='relation between heart disease and resting electrocardiograhic',xlabel='resting electrocardiograhic',ylabel='numbers',width=550,height=550,legend_cols=2)

#now trying to get the categorial value
categorial_values=[]
non_categorial_values=[]
for i in heart_data.columns:
    if(len(heart_data[i].unique())<=10):
        categorial_values.append(i)
    else:
        non_categorial_values.append(i)

categorial_values

['sex', 'cp', 'fbs', 'restecg', 'exang', 'slope', 'ca', 'thal', 'target']

non_categorial_values

['age', 'trestbps', 'chol', 'thalach', 'oldpeak']

#plotting the histogram data against the catgegorial values

plt.figure(figsize=(15,5))


for i,j in enumerate (categorial_values,1):
    plt.subplot(3,3,i)
    heart_data[heart_data["target"]==0][j].hist(bins=35,color='blue',label="Healthy patient",alpha=0.6)
    heart_data[heart_data["target"]==0][j].hist(bins=35,color='Red',label="non-Healthy patient",alpha=0.6)
    plt.legend()
    plt.xlabel(j)

#plotting the histogram data against the non-catgegorial values

plt.figure(figsize=(15,5))


for i,j in enumerate (non_categorial_values,1):
    plt.subplot(3,3,i)
    heart_data[heart_data["target"]==0][j].hist(bins=35,color='blue',label="Healthy patient",alpha=0.6)
    heart_data[heart_data["target"]==0][j].hist(bins=35,color='Red',label="non-Healthy patient",alpha=0.6)
    plt.legend()
    plt.xlabel(j)

plt.figure(figsize=(10,10))

plt.scatter(heart_data.age[heart_data.target==1],heart_data.thalach[heart_data.target==1],c="red")


plt.scatter(heart_data.age[heart_data.target==0],heart_data.thalach[heart_data.target==0],c="blue")

plt.title("heart disease relation between age and the max heart rate")
plt.xlabel("Age")
plt.ylabel("maximum heart date")
plt.legend(["Non-Healthy","Healthy"]);

corrleation_matrix=heart_data.corr()
fig,ax=plt.subplots(figsize=(10,10))
ax=sns.heatmap(corrleation_matrix,annot=True,linewidths=0.8,fmt=".2f",cmap="YlGnBu");
bottom,top=ax.get_ylim()
ax.set_ylim(bottom+0.5,top-0.5)

(14.5, -0.5)

heart_data.drop('target',axis=1).corrwith(heart_data.target).hvplot.barh(width=500,height=500,title="correlatiob between heart disease and the categroial features",
ylabel="correlation", xlabel="Categorial features",)

# now creating the features that based upon it we are going to determine the result of the heart disease['Target'], along with
# splitting the target which is the result

#Features_data

	age	sex	cp	trestbps	chol	fbs	restecg	thalach	exang	oldpeak	slope	ca	thal
0	63	1	3	145	233	1	0	150	0	2.3	0	0	1
1	37	1	2	130	250	0	1	187	0	3.5	0	0	2
2	41	0	1	130	204	0	0	172	0	1.4	2	0	2
3	56	1	1	120	236	0	1	178	0	0.8	2	0	2
4	57	0	0	120	354	0	1	163	1	0.6	2	0	2
...	...	...	...	...	...	...	...	...	...	...	...	...	...
298	57	0	0	140	241	0	1	123	1	0.2	1	0	3
299	45	1	3	110	264	0	1	132	0	1.2	1	0	3
300	68	1	0	144	193	1	1	141	0	3.4	1	2	3
301	57	1	0	130	131	0	1	115	1	1.2	1	1	3
302	57	0	1	130	236	0	0	174	0	0.0	1	1	2

303 rows × 13 columns

Target_data

0      1
1      1
2      1
3      1
4      1
      ..
298    0
299    0
300    0
301    0
302    0
Name: target, Length: 303, dtype: int64

# The data is now reserved in the variables

# now we have to feed the data to our machine learning algorithm, but before that we need to split the data into train data and 
# test data in order to work on the model

#Features_data_train,Features_data_test,Target_data_train,Target_data_test=train_test_split(Features_data,Target_data, test_size=0.3, stratify=Target_data, random_state=42)

#Features_data_train

#checking the shape after the train-test model

##print(Features_data.shape,Features_data_train.shape,Features_data_test.shape)

now train the maching learning model to feed it the data so it became trained, we will use logistic regression model

#Logistic_model=LogisticRegression()

# trying to train the machine learning model using the train data

#Logistic_model.fit(Features_data_train,Target_data_train)

# train the model to find the relation between the features and the target in order to have the model trained

# Evaluating the model to check how it will perform 

#Features_data_train_perdiction = Logistic_model_fit.predict(Features_data_train)
#checking the score of the train data predicted
#Accuracy_score_of_predicted_train_data= accuracy_score(Features_data_train_perdiction, Target_data_train)

#Accuracy_score_of_predicted_train_data

# getting the accuracy score of the training data

#Features_data_test_perdiction = Logistic_model_fit.predict(Features_data_test)
#checking the score of the test data predicted
#Accuracy_score_of_predicted_test_data= accuracy_score(Features_data_test_perdiction, Target_data_test)

#Accuracy_score_of_predicted_test_data

# now building the predictio system to check the health state of the patient

#Criteria_data=(38,1,2,138,175,0,1,173,0,0,2,4,2)
# getting the data that based on it we will determine if the target is one or not

#Criteria_data_shaped=np.asarray(Criteria_data)

# converting the data into array so we could work on it

# now trying to reshape the value to get one value only

#Criteria_data_shaped_one=Criteria_data_shaped.reshape(1,-1)

#Prediction_of_model=Logistic_model.predict(Criteria_data_shaped_one)

# print(Prediction_of_model)

# if(Prediction_of_model[0]==1):
#     print("the person is having a heart disease")
# else:
#     print("the person is healthy and not having a defect")

# the system have now predicted that the patient is not healthy and having a defect which is true if we checked the target value 
# in the original dataset

#categorial_values.remove('target')
#heart_data = pd.get_dummies(heart_data, columns = categorial_values)

 from sklearn.preprocessing import StandardScaler

s_sc = StandardScaler()
col_to_scale = ['age', 'trestbps', 'chol', 'thalach', 'oldpeak']
heart_data[col_to_scale] = s_sc.fit_transform(heart_data[col_to_scale])

# heart_data.head()

def scoreshow(model,Features_data_train,Target_data_train,Features_data_test,Target_data_test,train=True):
    if (train==True):
        Features_data_train_perdiction = model.predict(Features_data_train)
        Accuracy_score_of_predicted_train_data= accuracy_score(Target_data_train, Features_data_train_perdiction)
        Report_train=pd.DataFrame(classification_report(Target_data_train,Features_data_train_perdiction,output_dict=True))
        print(f"Accuracy Score: {Accuracy_score_of_predicted_train_data}")
        #print("the accuracy score = ", {Accuracy_score_of_predicted_train_data})
        print(f"Report of classification:\n {Report_train}")
        print(f"confusion matrix: \n {confusion_matrix(Target_data_train,Features_data_train_perdiction)}\n")
    elif(train==False):
        Features_data_test_perdiction = model.predict(Features_data_test)
        Accuracy_score_of_predicted_test_data= accuracy_score(Target_data_test, Features_data_test_perdiction)
        Report_test=pd.DataFrame(classification_report(Target_data_test,Features_data_test_perdiction,output_dict=True))
        print(f"Accuracy Score: {Accuracy_score_of_predicted_test_data}")
        #print("the accuracy score = ", {Accuracy_score_of_predicted_test_data})
        print(f"Report of classification\n {Report_test}")
        print(f"confusion matrix :\n {confusion_matrix(Target_data_test,Features_data_test_perdiction)}\n") 

Features_data=heart_data.drop(columns='target',axis=1)
Target_data= heart_data['target']


Features_data_train,Features_data_test,Target_data_train,Target_data_test=train_test_split(Features_data,Target_data, test_size=0.3, stratify=Target_data, random_state=42)

The logistic model

Logistic_model=LogisticRegression()
Logistic_model.fit(Features_data_train,Target_data_train)

print("With the train data\n\n")
scoreshow(Logistic_model,Features_data_train,Target_data_train,Features_data_test,Target_data_test,train=True)

print("Without the train data\n\n")

scoreshow(Logistic_model,Features_data_train,Target_data_train,Features_data_test,Target_data_test,train=False)

With the train data


Accuracy Score: 0.8773584905660378
Report of classification:
                    0           1  accuracy   macro avg  weighted avg
precision   0.908046    0.856000  0.877358    0.882023      0.879813
recall      0.814433    0.930435  0.877358    0.872434      0.877358
f1-score    0.858696    0.891667  0.877358    0.875181      0.876581
support    97.000000  115.000000  0.877358  212.000000    212.000000
confusion matrix: 
 [[ 79  18]
 [  8 107]]

Without the train data


Accuracy Score: 0.7582417582417582
Report of classification
                    0          1  accuracy  macro avg  weighted avg
precision   0.756757   0.759259  0.758242   0.758008      0.758132
recall      0.682927   0.820000  0.758242   0.751463      0.758242
f1-score    0.717949   0.788462  0.758242   0.753205      0.756692
support    41.000000  50.000000  0.758242  91.000000     91.000000
confusion matrix :
 [[28 13]
 [ 9 41]]

K-nearest model

from sklearn.neighbors import KNeighborsClassifier
KNeighbors_model=KNeighborsClassifier()
KNeighbors_model.fit(Features_data_train,Target_data_train)
print("With the train data\n\n")

scoreshow(KNeighbors_model,Features_data_train,Target_data_train,Features_data_test,Target_data_test,train=True)
print("Without the train data\n\n")

scoreshow(KNeighbors_model,Features_data_train,Target_data_train,Features_data_test,Target_data_test,train=False)

With the train data


Accuracy Score: 0.8679245283018868
Report of classification:
                    0           1  accuracy   macro avg  weighted avg
precision   0.887640    0.853659  0.867925    0.870649      0.869207
recall      0.814433    0.913043  0.867925    0.863738      0.867925
f1-score    0.849462    0.882353  0.867925    0.865908      0.867304
support    97.000000  115.000000  0.867925  212.000000    212.000000
confusion matrix: 
 [[ 79  18]
 [ 10 105]]

Without the train data


Accuracy Score: 0.7692307692307693
Report of classification
                    0          1  accuracy  macro avg  weighted avg
precision   0.794118   0.754386  0.769231   0.774252      0.772287
recall      0.658537   0.860000  0.769231   0.759268      0.769231
f1-score    0.720000   0.803738  0.769231   0.761869      0.766010
support    41.000000  50.000000  0.769231  91.000000     91.000000
confusion matrix :
 [[27 14]
 [ 7 43]]

Support vector machine

from sklearn.svm import SVC
SVM_model=SVC(kernel='rbf',gamma=0.1,C=1.0)
SVM_model.fit(Features_data_train,Target_data_train)
print("With the train data\n\n")

scoreshow(SVM_model,Features_data_train,Target_data_train,Features_data_test,Target_data_test,train=True)
print("Without the train data\n\n")

scoreshow(SVM_model,Features_data_train,Target_data_train,Features_data_test,Target_data_test,train=False)

With the train data


Accuracy Score: 0.910377358490566
Report of classification:
                    0           1  accuracy   macro avg  weighted avg
precision   0.933333    0.893443  0.910377    0.913388      0.911695
recall      0.865979    0.947826  0.910377    0.906903      0.910377
f1-score    0.898396    0.919831  0.910377    0.909113      0.910023
support    97.000000  115.000000  0.910377  212.000000    212.000000
confusion matrix: 
 [[ 84  13]
 [  6 109]]

Without the train data


Accuracy Score: 0.7692307692307693
Report of classification
                    0          1  accuracy  macro avg  weighted avg
precision   0.794118   0.754386  0.769231   0.774252      0.772287
recall      0.658537   0.860000  0.769231   0.759268      0.769231
f1-score    0.720000   0.803738  0.769231   0.761869      0.766010
support    41.000000  50.000000  0.769231  91.000000     91.000000
confusion matrix :
 [[27 14]
 [ 7 43]]

Decision Tree Classifier Model

from sklearn.tree import DecisionTreeClassifier


Tree_decision_model = DecisionTreeClassifier(random_state=42)
Tree_decision_model.fit(Features_data_train, Target_data_train)
print("With the train data\n\n")

scoreshow(Tree_decision_model, Features_data_train, Target_data_train, Features_data_test, Target_data_test, train=True)
print("Without the train data\n\n")

scoreshow(Tree_decision_model, Features_data_train, Target_data_train, Features_data_test, Target_data_test, train=False)

With the train data


Accuracy Score: 1.0
Report of classification:
               0      1  accuracy  macro avg  weighted avg
precision   1.0    1.0       1.0        1.0           1.0
recall      1.0    1.0       1.0        1.0           1.0
f1-score    1.0    1.0       1.0        1.0           1.0
support    97.0  115.0       1.0      212.0         212.0
confusion matrix: 
 [[ 97   0]
 [  0 115]]

Without the train data


Accuracy Score: 0.7252747252747253
Report of classification
                    0          1  accuracy  macro avg  weighted avg
precision   0.681818   0.765957  0.725275   0.723888      0.728049
recall      0.731707   0.720000  0.725275   0.725854      0.725275
f1-score    0.705882   0.742268  0.725275   0.724075      0.725874
support    41.000000  50.000000  0.725275  91.000000     91.000000
confusion matrix :
 [[30 11]
 [14 36]]

Random Forest Classifier Model

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import RandomizedSearchCV


Random_forest_classifier=RandomForestClassifier(n_estimators=1000, random_state=42)

Random_forest_classifier.fit(Features_data_train,Target_data_train)
print("With the train data\n\n")

scoreshow(Random_forest_classifier, Features_data_train, Target_data_train, Features_data_test, Target_data_test, train=True)
print("Without the train data\n\n")

scoreshow(Random_forest_classifier, Features_data_train, Target_data_train, Features_data_test, Target_data_test, train=False)

With the train data


Accuracy Score: 1.0
Report of classification:
               0      1  accuracy  macro avg  weighted avg
precision   1.0    1.0       1.0        1.0           1.0
recall      1.0    1.0       1.0        1.0           1.0
f1-score    1.0    1.0       1.0        1.0           1.0
support    97.0  115.0       1.0      212.0         212.0
confusion matrix: 
 [[ 97   0]
 [  0 115]]

Without the train data


Accuracy Score: 0.8021978021978022
Report of classification
                    0          1  accuracy  macro avg  weighted avg
precision   0.810811   0.796296  0.802198   0.803554      0.802836
recall      0.731707   0.860000  0.802198   0.795854      0.802198
f1-score    0.769231   0.826923  0.802198   0.798077      0.800930
support    41.000000  50.000000  0.802198  91.000000     91.000000
confusion matrix :
 [[30 11]
 [ 7 43]]

Xg boost classifier

from xgboost import XGBClassifier

xgboost_model=XGBClassifier(use_label_encoder=False)
xgboost_model.fit(Features_data_train,Target_data_train)

print("With the train data\n\n")

scoreshow(xgboost_model, Features_data_train, Target_data_train, Features_data_test, Target_data_test, train=True)
print("Without the train data\n\n")

scoreshow(xgboost_model, Features_data_train, Target_data_train, Features_data_test, Target_data_test, train=False)

[15:32:38] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
With the train data


Accuracy Score: 1.0
Report of classification:
               0      1  accuracy  macro avg  weighted avg
precision   1.0    1.0       1.0        1.0           1.0
recall      1.0    1.0       1.0        1.0           1.0
f1-score    1.0    1.0       1.0        1.0           1.0
support    97.0  115.0       1.0      212.0         212.0
confusion matrix: 
 [[ 97   0]
 [  0 115]]

Without the train data


Accuracy Score: 0.7362637362637363
Report of classification
                    0      1  accuracy  macro avg  weighted avg
precision   0.707317   0.76  0.736264   0.733659      0.736264
recall      0.707317   0.76  0.736264   0.733659      0.736264
f1-score    0.707317   0.76  0.736264   0.733659      0.736264
support    41.000000  50.00  0.736264  91.000000     91.000000
confusion matrix :
 [[29 12]
 [12 38]]

Criteria_data=(38,1,2,138,175,0,1,173,0,0,2,4,2)
# getting the data that based on it we will determine if the target is one or not

Criteria_data_shaped=np.asarray(Criteria_data)

#converting the data into array so we could work on it

Criteria_data_shaped_one=Criteria_data_shaped.reshape(1,-1)

Prediction_of_model=Logistic_model.predict(Criteria_data_shaped_one)

Prediction_of_model

array([1], dtype=int64)

# as we can see the model here predicted that the patient is having heart disease from feeding the data to the training model